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BLANNOTATOR:基于同源性的细菌蛋白功能增强预测。

BLANNOTATOR: enhanced homology-based function prediction of bacterial proteins.

机构信息

Institute of Biotechnology, University of Helsinki, Helsinki, Finland.

出版信息

BMC Bioinformatics. 2012 Feb 15;13:33. doi: 10.1186/1471-2105-13-33.

Abstract

BACKGROUND

Automated function prediction has played a central role in determining the biological functions of bacterial proteins. Typically, protein function annotation relies on homology, and function is inferred from other proteins with similar sequences. This approach has become popular in bacterial genomics because it is one of the few methods that is practical for large datasets and because it does not require additional functional genomics experiments. However, the existing solutions produce erroneous predictions in many cases, especially when query sequences have low levels of identity with the annotated source protein. This problem has created a pressing need for improvements in homology-based annotation.

RESULTS

We present an automated method for the functional annotation of bacterial protein sequences. Based on sequence similarity searches, BLANNOTATOR accurately annotates query sequences with one-line summary descriptions of protein function. It groups sequences identified by BLAST into subsets according to their annotation and bases its prediction on a set of sequences with consistent functional information. We show the results of BLANNOTATOR's performance in sets of bacterial proteins with known functions. We simulated the annotation process for 3090 SWISS-PROT proteins using a database in its state preceding the functional characterisation of the query protein. For this dataset, our method outperformed the five others that we tested, and the improved performance was maintained even in the absence of highly related sequence hits. We further demonstrate the value of our tool by analysing the putative proteome of Lactobacillus crispatus strain ST1.

CONCLUSIONS

BLANNOTATOR is an accurate method for bacterial protein function prediction. It is practical for genome-scale data and does not require pre-existing sequence clustering; thus, this method suits the needs of bacterial genome and metagenome researchers. The method and a web-server are available at http://ekhidna.biocenter.helsinki.fi/poxo/blannotator/.

摘要

背景

自动化功能预测在确定细菌蛋白质的生物学功能方面发挥了核心作用。通常,蛋白质功能注释依赖于同源性,并且功能是从具有相似序列的其他蛋白质推断出来的。这种方法在细菌基因组学中很流行,因为它是少数几种适用于大型数据集的方法之一,而且它不需要额外的功能基因组学实验。然而,现有的解决方案在许多情况下会产生错误的预测,尤其是当查询序列与注释的源蛋白质的同一性水平较低时。这个问题迫切需要改进基于同源性的注释。

结果

我们提出了一种自动化的细菌蛋白质序列功能注释方法。基于序列相似性搜索,BLANNOTATOR 可以准确地用蛋白质功能的一行摘要描述来注释查询序列。它根据注释将通过 BLAST 识别的序列分组到子集,并且根据具有一致功能信息的一组序列进行预测。我们展示了 BLANNOTATOR 在具有已知功能的细菌蛋白质组中的性能结果。我们使用在查询蛋白质的功能特征化之前的状态下的数据库模拟了 3090 个 SWISS-PROT 蛋白质的注释过程。对于这个数据集,我们的方法优于我们测试的其他五种方法,即使在没有高度相关的序列命中的情况下,性能也得到了保持。我们进一步通过分析乳杆菌 crispatus 菌株 ST1 的假定蛋白质组来证明我们工具的价值。

结论

BLANNOTATOR 是一种用于细菌蛋白质功能预测的准确方法。它适用于基因组规模的数据,并且不需要预先存在的序列聚类;因此,这种方法适合细菌基因组和宏基因组研究人员的需求。该方法和一个网络服务器可在 http://ekhidna.biocenter.helsinki.fi/poxo/blannotator/ 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d990/3386020/e01af2e0470d/1471-2105-13-33-1.jpg

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